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| Precyzja× | Dokładność (Accuracy)× | |
|---|---|---|
| Dziedzina | Ocena modeli | Ocena modeli |
| Rodzina | MCDM | MCDM |
| Rok powstania | 20th century | 20th century |
| Twórca | Historical statistical foundations | Historical statistical foundations |
| Typ | Evaluation metric | Evaluation metric |
| Źródło pierwotne | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ |
| Inne nazwy | Positive Predictive Value, PPV | Overall Accuracy, Correct Classification Rate |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | Precision measures the proportion of positive predictions that were actually correct. It answers the question: 'Of all the cases we predicted as positive, how many were truly positive?' Precision is critical in scenarios where false positives are costly. | Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class. |
| ScholarGateZbiór danych ↗ |
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